A tailored course, built for your situation
Mastering OWASP; A Step-by-Step Guide to Secure Machine Learning Deployment
Build defensible, production-grade AI systems with fewer reworks and higher stakeholder trust.
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Model review cycles that stall deployment timelines due to incomplete or inconsistent security documentation.
The situation this course is for
ML engineers spend critical time reworking model artifacts to meet shifting security review standards. The lack of a consistent, pre-emptive framework leads to delayed deployments, repeated feedback loops, and eroded stakeholder confidence, especially under tight regulatory or internal audit cycles.
Who this is for
Senior Machine Learning Engineer at a large tech firm shipping AI models into production, facing increasing scrutiny from internal security teams and platform governance bodies.
Who this is not for
Researchers focused on novel algorithms without deployment intent, or engineers working in non-regulated consumer apps with minimal security oversight.
What you walk away with
- Produce model security documentation that passes internal review the first time
- Reduce rework cycles by aligning with OWASP AI Security standards upfront
- Build stakeholder trust through consistent, auditable model assurance packages
- Anticipate common feedback points in security reviews before submission
- Integrate security-by-design patterns into daily ML workflow without slowing innovation
The 12 modules (with all 144 chapters)
Module 1. The State of AI Security right now
Understand the evolving threat landscape for machine learning systems and how OWASP's guidelines are becoming the de facto baseline for secure deployment across major platforms.
12 chapters in this module
- Overview of recent AI-related security incidents in large tech
- How OWASP shifted from web to AI threat modeling
- Why internal security teams now require formal documentation
- The rise of model supply chain vulnerabilities
- Key differences between traditional app security and ML security
- How Meta’s internal review cycles reflect industry trends
- The role of red teaming in pre-deployment validation
- Common failure points in model security assessments
- From research prototype to production readiness
- How security debt accumulates in ML pipelines
- The cost of delayed deployments due to compliance gaps
- Why first-time approval matters for team velocity
Module 2. OWASP Top 10 for AI: Core Principles
Break down each of the OWASP Top 10 AI risks with concrete examples relevant to production ML systems at scale.
12 chapters in this module
- Understanding Model Theft and data leakage risks
- Preventing prompt injection in generative pipelines
- Securing training data provenance and chain of custody
- Detecting and mitigating data poisoning attacks
- Validating model integrity across environments
- Hardening APIs used for model serving
- Managing access controls for fine-tuning endpoints
- Avoiding insecure output handling in LLM chains
- Mitigating overreliance on unverified model outputs
- Documenting model limitations for downstream consumers
- Ensuring accountability in autonomous decisioning
- Building audit trails into inference pipelines
Module 3. From Framework to Actionable Checklist
Translate OWASP guidelines into a practical, repeatable checklist tailored to ML deployment workflows.
12 chapters in this module
- Mapping OWASP controls to ML lifecycle phases
- Creating a pre-submission security validation gate
- Developing a standardized model security questionnaire
- Integrating checklist items into CI/CD pipelines
- Automating evidence collection for common controls
- Defining ownership for each security artifact
- Aligning with internal red team expectations
- Prioritizing high-impact controls for fast wins
- Versioning security checklists alongside model updates
- Tracking compliance status across model inventory
- Using the checklist to scope security reviews
- Avoiding checklist bloat with risk-based trimming
Module 4. Building the Model Security Package
Assemble a complete, defensible package that anticipates reviewer questions and reduces back-and-forth.
12 chapters in this module
- Core components of a production-ready security submission
- Writing clear model purpose and scope statements
- Documenting data sources and preprocessing logic
- Describing training compute environment securely
- Detailing hyperparameter selection process
- Recording model evaluation methodology
- Explaining bias detection and mitigation steps
- Summarizing adversarial robustness testing
- Listing third-party dependencies and licenses
- Providing API security configuration details
- Including monitoring and drift detection plans
- Attaching human oversight protocols
Module 5. Anticipating Reviewer Feedback
Learn the most common pushbacks from security reviewers and how to preempt them in your documentation.
12 chapters in this module
- Top 5 reasons for model review rejection
- How to clarify model boundaries and assumptions
- Avoiding vague claims about model performance
- Justifying data sampling choices transparently
- Explaining model update triggers and rollback plans
- Clarifying monitoring thresholds and alerting logic
- Addressing explainability expectations early
- Handling sensitive attribute usage disclosures
- Responding to supply chain security concerns
- Preparing for model reuse and transfer learning questions
- Defending architecture choices with trade-off analysis
- Providing evidence of stress testing results
Module 6. Integrating Security into MLOps
Embed security validation steps directly into automated ML pipelines to catch issues early.
12 chapters in this module
- Adding security gates to model training workflows
- Automating data provenance tracking
- Validating model signatures before deployment
- Scanning for known vulnerabilities in dependencies
- Enforcing secure model packaging standards
- Generating security metadata during training
- Running automated OWASP control checks
- Flagging high-risk patterns in model behavior
- Integrating with internal threat intelligence
- Using model cards as living security documents
- Versioning security artifacts alongside models
- Creating audit-ready export bundles automatically
Module 7. Writing Defensible Model Narratives
Craft clear, credible stories around model design and risk mitigation that build reviewer confidence.
12 chapters in this module
- Structuring the executive summary for clarity
- Explaining technical trade-offs to non-experts
- Using concrete examples to illustrate safeguards
- Avoiding overstatement of model capabilities
- Acknowledging limitations proactively
- Framing uncertainty in probabilistic terms
- Linking risk controls to specific threat vectors
- Showing traceability from design to implementation
- Using diagrams to clarify data flows
- Describing fallback mechanisms clearly
- Aligning language with internal policy documents
- Maintaining tone of professional confidence
Module 8. Collaborating with Security Teams
Improve cross-functional alignment by speaking the shared language of risk and control.
12 chapters in this module
- Understanding the security reviewer’s incentives
- Asking better questions during pre-submission syncs
- Translating ML concepts into risk terms
- Building trust through consistency over time
- Scheduling early checkpoints to avoid bottlenecks
- Using common frameworks to reduce misalignment
- Responding to feedback with precision
- Knowing when to escalate vs. revise
- Sharing templates across ML teams
- Creating feedback loops to improve checklists
- Documenting exceptions with justification
- Maintaining a shared glossary of terms
Module 9. Versioning and Maintaining Compliance
Keep model security documentation accurate and up-to-date through iterative development.
12 chapters in this module
- Defining what triggers a full re-review
- Managing incremental updates efficiently
- Tracking changes to data sources and pipelines
- Updating security packages with model versions
- Automating compliance status checks
- Handling model retirement securely
- Preserving historical documentation
- Auditing access to model artifacts
- Managing access revocation for team changes
- Ensuring backup and recovery of key documents
- Meeting data retention policy requirements
- Supporting internal audit requests efficiently
Module 10. Scaling Security Across Model Portfolios
Apply consistent standards across multiple models and teams without slowing innovation.
12 chapters in this module
- Creating a centralized model inventory
- Categorizing models by risk tier
- Applying proportional review rigor
- Developing reusable security patterns
- Training new team members on standards
- Sharing validated templates and examples
- Implementing peer review workflows
- Using automation to enforce baseline checks
- Monitoring compliance at scale
- Reporting aggregate risk posture to leadership
- Optimizing resource allocation for reviews
- Reducing duplication across similar models
Module 11. Preparing for External Audits
Ensure internal readiness for external scrutiny from regulators or partners.
12 chapters in this module
- Understanding external auditor expectations
- Mapping internal controls to regulatory requirements
- Gathering evidence for third-party review
- Conducting mock audit exercises
- Preparing teams for interview rounds
- Organizing documentation for rapid access
- Redacting sensitive information appropriately
- Maintaining chain of custody for artifacts
- Demonstrating continuous improvement
- Showing alignment with industry standards
- Responding to follow-up requests efficiently
- Closing audit findings with permanent fixes
Module 12. Leading by Example in Secure AI
Become the internal reference for high-quality, review-ready model deployments.
12 chapters in this module
- Mentoring peers on security best practices
- Advocating for secure-by-design culture
- Contributing to internal policy development
- Sharing lessons from past reviews
- Improving organizational templates
- Recognizing team members for quality submissions
- Driving adoption of automated tooling
- Balancing speed and security effectively
- Measuring impact through review success rate
- Building reputation as a trusted deployer
- Influencing tooling roadmaps with feedback
- Setting the standard for future hires
How this maps to your situation
- Model deployment under internal security review
- Documentation rework due to compliance gaps
- Cross-functional friction with security teams
- Need for consistent, first-time approval
Before vs. after
Before
Spending cycles fixing model documentation after feedback, facing delays and eroded trust.
After
Submitting complete, defensible packages that pass review the first time, building credibility and momentum.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 90 minutes per module, designed to be completed over 12 weekends or intensively in one week.
If nothing changes
Continuing with ad-hoc documentation increases the likelihood of deployment delays, escalations, and missed opportunities to lead on secure AI practices within the organization.
How this compares to the alternatives
Unlike generic AI ethics courses or broad cybersecurity trainings, this course focuses specifically on the practical, artifact-level work required to get ML models approved quickly and confidently under modern security review processes.
Frequently asked
Is this course focused on offensive security or defensive design?
It's focused on defensive design , building models and documentation that meet internal security review standards proactively.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover regulatory compliance like GDPR or CCPA?
It touches on them as context, but the focus is on OWASP-based security controls that underpin compliance in ML systems.
$199 one-time. Approximately 90 minutes per module, designed to be completed over 12 weekends or intensively in one week..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours